Impact of SWMM Catchment Discretization: Case Study in Syracuse, New York
Publication: Journal of Hydrologic Engineering
Volume 19, Issue 1
Abstract
This study examined how the level of catchment discretization influenced the model parameterization and output uncertainty of the Storm Water Management Model (SWMM) 5.0. Two catchment delineations for a highly urbanized sewershed in Syracuse, New York were developed: (1) the macroscale model containing a minimum required number of subcatchments to retain the original sewer network properties; and (2) the microscale model in which each subcatchment was defined for a unique soil and land-use combination. For both scales, the model parameters were calibrated and the uncertainty of model outputs was quantified using the generalized likelihood uncertainty estimation (GLUE) methodology. Then, calibrated posterior parameter sets were applied at micro- and macroscales individually to a second sewershed, which was also delineated at both micro- and macroscales, to test observed versus simulated flows. The results indicated that the catchment disaggregation level had a great impact on both parameterization and simulation results, and the majority of the parameters were sensitive to the modeling scales. Overall, the posterior parameters calibrated based on the microdelineation resulted in a higher degree of reduction in output uncertainties for both calibrated and validated sewersheds. Hence, it can be argued that the calibrated parameters obtained, based upon the macrodelineation, would result in reduced confidence in simulated runoff for another site unique in its characteristics, whereas the posterior parameters derived from the microdelineation could provide a higher confidence level in terms of parameter transferability for modeling other, particularly ungauged sites.
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Acknowledgments
We gratefully acknowledge the National Science Foundation Award BSC-0948952 for an Urban Long Term Research Area Exploratory project (ULTRA-EX) that supported and inspired this research. We also want to thank the Onondaga County Water and Environment Program and CH2MHILL, Syracuse, for the provision of monitored flow data.
References
Baker, W. L. (1989). “A review of models of landscape change.” Landscape Ecol., 2(2), 111–133.
Bales, J., and Betson, R. (1982). “The curve number as a hydrologic index.” Rainfall-runoff relationships, V. P. Singh, ed., Water Resources, Highlands Ranch, CO, 371–386.
Barco, J., Wong, K. M., and Stenstrom, M. K. (2008). “Automatic calibration of the U.S.EPA SWMM model for a large urban catchment.” J. Hydraul. Eng., 466–474.
Bates, S. C., Raftery, A. E., and Cullen, A. C. (2000). “Bayesian uncertainty assessment in deterministic models for environmental risk assessment.”, U.S. EPA, Washington, DC.
Beven, K. (1989). “Changing ideas in hydrology—The case of physically-based models.” J. Hydrol., 105(1–2), 157–172.
Beven, K. (2006). “A manifesto for the equifinality thesis.” J. Hydrol., 320(1–2), 18–36.
Beven, K., and Binley, A. (1992). “The future of distributed models: Model calibration and uncertainty prediction.” Hydrol. Process., 6(3), 279–298.
Blazkova, S., Beven, K. J., and Kulasova, A. (2002). “On constraining TOPMODEL hydrograph simulations using partial saturated area information.” Hydrol. Process., 16(2), 441–458.
Box, G. E. P., and Tiao, G. C. (1992). Bayesian inference in statistical analysis, Wiley, New York.
Butts, M. B., Payne, J. T., Kristensen, M., and Madsen, H. (2004). “An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation.” J. Hydrol., 298(1–4), 242–266.
Choi, H. T., and Beven, K. (2007). “Multi-period and multi-criteria model conditioning to reduce prediction uncertainty in an application of TOPMODEL within the GLUE framework.” J. Hydrol., 332(3–4), 316–336.
Chow, V. T., Maidment, D. R., and Mays, L. W. (1988). Applied hydrology, McGraw-Hill, New York, NY.
Dankenbring, S., and Mays, D. (2009). “Catchment discretization in the Colorado urban hydrograph procedure: A case study in the East Toll Gate Creek watershed.”, Arapahoe County, CO.
Fang, T., and Ball, J. E. (2007). “Evaluation of spatially variable control parameters in a complex catchment modeling system: a genetic algorithm application.” J. Hydroinform., 9(3), 163–173.
Ghosh, I., and Hellweger, F. L. (2012). “Effects of spatial resolution in urban hydrologic simulations.” J. Hydrol. Eng., 129–137.
Gironás, J., Roesner, L. A., Davis, J., and Rossman, L. A. (2009). Storm Water Management Model applications manual, National Risk Management Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati.
Grigg, N. S. (1996). Water resources management: Principles, regulations, and cases, McGraw-Hill, New York, NY, 311–314.
Guo, J. C. Y., and Urbonas, B. (2008). “Consistency between CUHP and rational methods.” Urban Drainage and Flood Control District, Denver.
Hong, B., Strawderman, R. L., Swaney, D. P., and Weinstein, D. A. (2005). “Bayesian estimation of input parameters of a nitrogen cycle model applied to a forested reference watershed, Hubbard Brook watershed six.” Water Resour. Res., 41(3), W03007.
Hong, B., Swaney, D. P., and Weinstein, D. A. (2006). “Simulating spatial nitrogen dynamics in a forested reference watershed. Hubbard Brook Watershed 6, New Hampshire, USA.” Landscape Ecol., 21(2), 195–211.
Horton, R. E. (1935). Surface runoff phenomena, Edwards Brothers, Inc, Ann Arbor, MI.
Huber, W. C., and Dickinson, R. E. (1992). “Storm water management model user’s manual, version 4.” U.S. Environmental Protection Agency, GA.
Huber, W. C., Dickinson, R. E., and Barnwell, T. O., Jr. (1988). “Storm water management model; version 4.” U.S. Environmental Protection Agency, Cincinnati, OH.
Huber, W. C., Heaney, J. P., Medina, M. A., Peltz, W. A., Sheikh, H., and Smith, G. F. (1975). “Storm water management model user’s manual, version II.”, U.S. Environmental Protection Agency, Cincinnati, OH.
Kazezyılmaz-Alhan, C. M., and Medina, M. A. (2007). “Kinematic and diffusion waves: Analytical and numerical solutions to overland and channel flow.” J. Hydraul. Eng., 217–228.
Kazezyılmaz-Alhan, C. M., Medina, M. A., and Rao, P. (2005). “On numerical modeling of overland flow.” Appl. Math. Comput., 166(3), 724–740.
Lamb, R., Beven, K., and Myrabų, S. (1998). “Use of spatially distributed water table observations to constrain uncertainty in a rainfall-runoff model.” Adv. Water Resour., 22(4), 305–317.
Leavesley, G. H., and Stannard, L. G. (1990). “Application of remotely sensed data in a distributed-parameter watershed model.” Proc., Workshop on Applications of Remote Sensing in Hydrology, National Hydrologic Research Center, Saskatoon, SK, 47–68.
Lighthill, M. J., and Whitham, G. B. (1955). “On kinematic waves. I. flood movement in long rivers.” Proc. R. Soc. London, Ser. A, 229(1178), 281–316.
MacArthur, R., and DeVries, J. J. (1993). “Introduction and application of kinematic wave routing techniques using HEC-1.” U.S. Army Corps of Engineers, Institute for Water Resources, Hydrologic Engineering Center, Vicksburg, MS.
Mamillapalli, S., Srinivasan, R., Arnold, J. G., and Engel, B. A. (1996). “Effect of spatial variability on basin scale modeling.” Proc., 3rd Int. Conf./Workshop on Integrating GIS and Environmental Modeling, National Center for Geographic Information and Analysis, Santa Barbara, CA.
Moore, I., and Gallant, J. (1991). “Overview of hydrologic and water quality modelling.” Modeling the fate of chemicals in the environment, I. Moore, ed., Centre for Resource and Environmental Studies, Australian National University, Canberra, 1–8.
MWH Soft, Inc. (2005). InfoSWMM user manual, MWH Soft Inc., Pasadena, CA.
Natural Resources Conservation Service (NRCS). (2010). “Soil Survey Geographic (SSURGO) database for Onondaga County, New York.” USDA, NRCS, Fort Worth, TX.
Overton, D. E., and Meadows, M. E. (1976). Stormwater modeling, Academic, New York.
Ratto, M., Tarantola, S., and Saltelli, A. (2001). “Sensitivity analysis in model calibration: GSA-GLUE approach.” Comput. Phys. Commun., 136(3), 212–224.
Rossman, L. A. (2010). “Storm water management model user’s manual, version 5.0.” National Risk Management Research Laboratory, Office of Research and Development, U.S. EPA, Cincinnati, OH.
Rubin, D. B. (1987). “The calculation of posterior distributions by data augmentation: Comment: A noniterative sampling/importance resampling alternative to the data augmentation algorithm for creating a few imputations when fractions of missing information are modest: The SIR algorithm.” J. Am. Stat. Assoc., 82(398), 543–546.
Rubin, D. B. (1988). “Using the SIR algorithm to simulate posterior distributions.” Bayesian Statistics 3, Oxford University Press, Oxford, U.K, 395–402.
Saltelli, A. (2002). “Making best use of model evaluations to compute sensitivity indices.” Comput. Phys. Commun., 145(2), 280–297.
Saltelli, A. (2004). Sensitivity analysis in practice: a guide to assessing scientific models, Wiley, New York.
Saltelli, A., Tarantola, S., and Campolongo, F. (2000). “Sensitivity analysis as an ingredient of modeling.” Stat. Sci., 15(4), 377–395.
Tsihrintzis, V. A., and Hamid, R. (1998). “Runoff quality prediction from small urban catchment using SWMM.” Hydrol. Process., 12(2), 311–329.
USDA Soil Conservation Service (SCS). (1986). “Urban hydrology for small watersheds.” Technical Release 55, 2nd Ed., NTIS PB87-101580,USDA SCS, Springfield, VA.
Xiong, L., and O’Connor, K. M. (2008). “An empirical method to improve the prediction limits of the GLUE methodology in rainfall–runoff modeling.” J. Hydrol., 349(1–2), 115–124.
Xiong, Y., and Melching, C. S. (2005). “Comparison of kinematic-wave and nonlinear reservoir routing of urban watershed runoff.” J. Hydrol. Eng., 39–49.
Zaghloul, N. A. (1981). “SWMM model and level of discretization.” J. Hydraul. Div., 107(11), 1535–1545.
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© 2014 American Society of Civil Engineers.
History
Received: Mar 28, 2012
Accepted: Dec 21, 2012
Published online: Dec 26, 2012
Discussion open until: May 26, 2013
Published in print: Jan 1, 2014
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